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Non-negative locality-constrained vocabulary tree for finger vein image retrieval 被引量:1
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作者 Kun SU Gongping YANG +2 位作者 Lu YANG Peng SU Yilong YIN 《Frontiers of Computer Science》 SCIE EI CSCD 2019年第2期318-332,共15页
Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research ... Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency. However, there is a large accumulative quantization error in the vocabulary tree (VT) model that may degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performanee and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performanee than other state-of-theart methods, while maintaining low time complexity. 展开更多
关键词 non-negative locality-constrained vocabulary tree finger VEIN image retrieval large scale inverted indexing
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